Population-based validation of the National Comprehensive Cancer Network recommendations for baseline imaging for bladder cancer: a case for routine baseline bone scan?
Bibliographic record
Abstract
AIM: This study aims at evaluating the performance of some of the imaging recommendations of the National Comprehensive Cancer Network (NCCN) for initial evaluation of bladder cancer. METHODS: Surveillance, epidemiology and end results program (2010-2015) was queried and patients with clinically (T1-T4) bladder cancer and complete information about clinical T/N (tumor/nodal) stage and metastatic sites were extracted. The following characteristics were evaluated in the current analysis: sensitivity, specificity, number needed to investigate (NNI), positive predictive value (PPV), negative predictive value and accuracy. RESULTS: According to the current NCCN guidelines, PPV (for the recognition of lung metastases) is 4.7% and NNI to detect one case of lung metastasis is 21.2. Similarly, PPV (for the recognition of liver metastases) is 3.1% and NNI to detect one case of liver metastasis is 32.2. Using a different imaging threshold (i.e., routinely imaging all patients >T2N0), PPV (for the recognition of lung metastases) is 10.4% and NNI to detect one case of lung metastasis is 9.6. Similarly, PPV (for the recognition of liver metastases) is 7% and NNI to detect one case of liver metastasis is 14.2. The above two thresholds were also evaluated for routine bone scanning. PPV (for the detection of one case of bone metastasis) is 5.3% using the first threshold and 11.2% using the second threshold. CONCLUSION: Imaging per current NCCN guidelines results in few patients with undetected asymptomatic lung or liver metastases. A routine baseline bone scan should be additionally considered for some asymptomatic patients with muscle-invasive disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".